summary(age) min=18.0, 1st Qut.=31.00, Median=43.00, Mean=45.61, 3rd Qut.=59.00, Max=89, NA's=197
These statistics show the respondent ages of the people to responded to the General Social Survey data. These summary statistics show that the first 25% of people who responded were below the age of 31. The median shows that at the very middle of the data, the age was 43. The mean shows that the average age of the respondents were 45.61 years old. The 3rd Quartile shows that the first 75% of the respondents were below the age of 59. The max says that the highest age of the respondents was 89 years old.
(Nice graphs, but you're just guessing what quartiles are. You're wrong. The first quartile is the value below which 25% of the observations fall, and the third quartile is the value above which 75% of the observations fall. These help us summarize the dispersion of the variable. Check these things. Use the book, use my lecture slides, use Google if you want! It's quick and easy, and beats guessing!)
summary(richppl) min=0.00, 1st Qut.=50.00, Median=50.00, Mean=58.28, 3rd Qut.=70.00, Max=100, NA's=279
These stats are quite similar to the welfareppl stats. Although people didn't have much feeling towards rich people, their third quarter ratings were equal in people's feelings about rich people. The mean was also very close with a deviation of only 2 degrees. So, on average, people held a more positive attitude towards the rich.
(How about standard deviations? The function looks like "sd(variablename" )
This scatter plot shows that there is no real correlation between the two feeling thermometers of rich people and people on welfare. I feel that when the correlation is calculated it will show this.
cor(nes$welfareppl, nes$richppl, use="complete.obs")= [1] 0.2959294
This correlation simply shows that these two statistics do not really affect each other much. There is no correlation between how people feel about welfare people and how people feel about rich people.
(It's not quite true to say there's exactly "no" correlation, is it? There's just a relatively small correlation.)
You do a fine job with most of these, although you should think twice when you narrate what's going on in the numbers. Double check your book, always try to be precise, and make sure you're exhausting the techniques you've learned to say the most you can with the data at hand.
summary(age) min=18.0, 1st Qut.=31.00, Median=43.00, Mean=45.61, 3rd Qut.=59.00, Max=89, NA's=197
These statistics show the respondent ages of the people to responded to the General Social Survey data. These summary statistics show that the first 25% of people who responded were below the age of 31. The median shows that at the very middle of the data, the age was 43. The mean shows that the average age of the respondents were 45.61 years old. The 3rd Quartile shows that the first 75% of the respondents were below the age of 59. The max says that the highest age of the respondents was 89 years old.
(Nice graphs, but you're just guessing what quartiles are. You're wrong. The first quartile is the value below which 25% of the observations fall, and the third quartile is the value above which 75% of the observations fall. These help us summarize the dispersion of the variable. Check these things. Use the book, use my lecture slides, use Google if you want! It's quick and easy, and beats guessing!)
summary(richppl) min=0.00, 1st Qut.=50.00, Median=50.00, Mean=58.28, 3rd Qut.=70.00, Max=100, NA's=279
These stats are quite similar to the welfareppl stats. Although people didn't have much feeling towards rich people, their third quarter ratings were equal in people's feelings about rich people. The mean was also very close with a deviation of only 2 degrees. So, on average, people held a more positive attitude towards the rich.
(How about standard deviations? The function looks like "sd(variablename" )
This scatter plot shows that there is no real correlation between the two feeling thermometers of rich people and people on welfare. I feel that when the correlation is calculated it will show this.
cor(nes$welfareppl, nes$richppl, use="complete.obs")= [1] 0.2959294
This correlation simply shows that these two statistics do not really affect each other much. There is no correlation between how people feel about welfare people and how people feel about rich people.
(It's not quite true to say there's exactly "no" correlation, is it? There's just a relatively small correlation.)
You do a fine job with most of these, although you should think twice when you narrate what's going on in the numbers. Double check your book, always try to be precise, and make sure you're exhausting the techniques you've learned to say the most you can with the data at hand.